Understanding and Overcoming Barriers: Learning Experiences of Undergraduate Sudanese Students at an Australian University
Bibliographic record
Abstract
An increase in migration of Sudanese and South Sudanese people to Australia due to civil unrest in their home country has increased the numbers of Sudanese students at university. Migrant experiences, particularly those of English as a second language, can impact negatively on education and learning. Inconsistencies between student scores on assessments and oral abilities in class prompted an exploratory project to identify barriers to success and create appropriate resources for students. The project utilised a multi-method approach to explore the experiences of the Sudanese students (n=22) enrolled at Edith Cowan University in Western Australia. Two quantitative scales examined motivations for learning and English Language Confidence. Interviews or focus groups explored the students’ perceptions of their learning and university experiences. The findings indicate that students are extrinsically motivated to study, confident in their language skills but required additional support to improve their written English. The barriers include socio-political factors unique to Sudanese students. Finally recommendations to assist these students are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".